Big and Complex Data Analysis: Methodologies and Applications (Contributions to Statistics)

This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential new developments in the field.

The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. Simultaneous variable selection and estimation is one of the key statistical problems involved in analyzing such big and complex data.

The purpose of this book is to stimulate research and foster interaction between researchers in the area of high-dimensional data analysis. More concretely, its goals are to: 1) highlight and expand the breadth of existing methods in big data and high-dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2) identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodologies for different application areas; and 3) facilitate collaboration between theoretical and subject-specific researchers.

本書傳達了高維度和複雜數據分析及相關領域中的一些驚喜、謎題和成功故事。經過同行評審的貢獻展示了變量選擇、估計和預測策略在許多有用模型中的最新進展，以及該領域的重要新發展。

現代技術的持續快速發展使科學家能夠收集越來越大且複雜的數據。例如，表觀基因組數據、基因組數據、蛋白質組數據、高分辨率圖像數據、高頻金融數據、功能和長期數據以及網絡數據等。同時進行變量選擇和估計是分析這些大型和複雜數據中的關鍵統計問題之一。

本書的目的是促進高維度數據分析領域的研究並促進研究人員之間的交流。更具體地說，它的目標是：1）突出和擴展大數據和高維度數據分析中現有方法的廣度，以及它們對數學和統計科學發展的潛力；2）確定正則化方法理論、算法開發和不同應用領域方法論的未來研究重點；以及3）促進理論和特定主題研究人員之間的合作。